def _call_single(self, x, a):
# Reshape kernels for efficient message-passing
kernel = tf.reshape(self.kernel, (-1, self.attn_heads * self.channels))
attn_kernel_self = ops.transpose(self.attn_kernel_self, (2, 1, 0))
attn_kernel_neighs = ops.transpose(self.attn_kernel_neighs, (2, 1, 0))
# Prepare message-passing
indices = a.indices
N = tf.shape(x, out_type=indices.dtype)[-2]
indices = ops.add_self_loops_indices(indices, N)
targets, sources = indices[:, 1], indices[:, 0]
# Update node features
x = K.dot(x, kernel)
x = tf.reshape(x, (-1, self.attn_heads, self.channels))
# Compute attention
attn_for_self = tf.reduce_sum(x * attn_kernel_self, -1)
attn_for_self = tf.gather(attn_for_self, targets)
attn_for_neighs = tf.reduce_sum(x * attn_kernel_neighs, -1)
attn_for_neighs = tf.gather(attn_for_neighs, sources)
attn_coef = attn_for_self + attn_for_neighs
attn_coef = tf.nn.leaky_relu(attn_coef, alpha=0.2)
attn_coef = ops.unsorted_segment_softmax(attn_coef, targets, N)
attn_coef = self.dropout(attn_coef)
attn_coef = attn_coef[..., None]
# Update representation
output = attn_coef * tf.gather(x, sources)
output = tf.math.unsorted_segment_sum(output, targets, N)
return output, attn_coef
def _call_single(self, X, A):
# Reshape kernels for efficient message-passing
kernel = tf.reshape(self.kernel, (-1, self.attn_heads * self.channels))
attn_kernel_self = ops.transpose(self.attn_kernel_self, (2, 1, 0))
attn_kernel_neighs = ops.transpose(self.attn_kernel_neighs, (2, 1, 0))
# Prepare message-passing
indices = A.indices
N = tf.shape(X, out_type=indices.dtype)[0]
indices = ops.sparse_add_self_loops(indices, N)
targets, sources = indices[:, -2], indices[:, -1]
# Update node features
X = ops.dot(X, kernel)
X = tf.reshape(X, (-1, self.attn_heads, self.channels))
# Compute attention
attn_for_self = tf.reduce_sum(X * attn_kernel_self, -1)
attn_for_self = tf.gather(attn_for_self, targets)
attn_for_neighs = tf.reduce_sum(X * attn_kernel_neighs, -1)
attn_for_neighs = tf.gather(attn_for_neighs, sources)
attn_coef = attn_for_self + attn_for_neighs
attn_coef = tf.nn.leaky_relu(attn_coef, alpha=0.2)
attn_coef = ops.unsorted_segment_softmax(attn_coef, targets, N)
attn_coef = self.dropout(attn_coef)
attn_coef = attn_coef[..., None]
# Update representation
output = attn_coef * tf.gather(X, sources)
output = ops.scatter_sum(targets, output, N)
return output, attn_coef